EAN: 9783319965611

MACHINE LEARNING FOR DYNAMIC SOFTWARE ANALYSIS ...

Machine learning of software artefacts is an emerging area of interaction between the machine
learning and software analysis communities. Increased productivity in software engineering
relies on the creation of new adaptive  scalable tools that can analyse large and continuously
changing software systems. These require new software analysis techniques based on machine
learning  such as learning-based software testing  invariant generation or code synthesis.
Machine learning is a powerful paradigm that provides novel approaches to automating the
generation of models and other essential software artifacts. This volume originates from a
Dagstuhl Seminar entitled Machine Learning for Dynamic Software Analysis: Potentials and Limits
held in April 2016. The seminar focused on fostering a spirit of collaboration in order to
share insights and to expand and strengthen the cross-fertilisation between the machine
learning and software analysis communities. The book provides an overview of the machine
learning techniques that can be used for software analysis and presents example applications of
their use. Besides an introductory chapter  the book is structured into three parts: testing
and learning  extension of automata learning  and integrative approaches.

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